Abstract: Policy makers make consequential choices on how to allocate limited health resources to improve population health. My research aims to find avenues to optimize the use of these resources. My talk addresses how to optimally allocate a limited vaccine supply over time, across population subgroups and between initial and booster doses, to control the spread of an infectious disease. By approximating epidemic dynamics, I develop simple analytical conditions characterizing the optimal vaccine allocation for four different objectives. I show that the approximated solution is an all-or-nothing allocation based on a prioritized list of population groups given by the analytical conditions. I illustrate my method with an example of COVID-19 vaccination, calibrated to epidemic data from New York State. Numerical computations show that my method achieves near-optimal results over a wide range of scenarios. Although black-box models are prevalent in the literature, my work shows that accuracy need not be sacrificed for interpretability. My methods provide practical, intuitive and accurate tools for decision makers as they allocate vaccines over time.